Another line of distributed optimization methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}
could be considered in this setting. Moreover, there are works that extend analysis of SGD type methods to FL settings [9]}, [10]}, [11]}. However, these algorithms are proposed for full device participation case which fails to satisfy one important aspect of FL. FedSVRG [1]} and DANE [3]} need gradient information from all devices at each round and they are not directly applicable to partial FL settings. For example, FedDANE [14]} is a version of DANE that works in partial participation. However, FedDANE performs worse than FedAvg empirically with partial participation [14]}. Similar to these works, FedPD [16]} method is proposed in distributed optimization with a different participation notion. FedPD activates either all devices or no devices per round which again fails to satisfy partial participation in FL.
